Triple
T28825531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian Open |
E727889
|
entity |
| Predicate | rankingPointsCategoryWomen |
P10762
|
FINISHED |
| Object | WTA 1000 points |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: WTA 1000 points | Statement: [Italian Open, rankingPointsCategoryWomen, WTA 1000 points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingPointsCategoryWomen Context triple: [Italian Open, rankingPointsCategoryWomen, WTA 1000 points]
-
A.
fashionCategory
Indicates the classification of an item into a specific fashion-related category or type (e.g., clothing, footwear, accessories).
-
B.
wearerRankCategory
Indicates the classification of the wearer's rank into a broader rank category (e.g., junior, senior, officer, etc.) within a ranking system.
-
C.
rankingCategory
chosen
Indicates the classification or type of ranking under which an entity is evaluated or ordered.
-
D.
womenSection
Indicates that something is designated as belonging to, located in, or associated with the women's section or area.
-
E.
styleCategory
Indicates the stylistic classification or genre category that an item, work, or entity belongs to.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f0319d09088190bbf14cdf1987792a |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
Created at: April 28, 2026, 6:35 a.m.